EXAMPLE - Rolling Functions

This example describes how to use the rolling computational functions:

ROLLINGSUM - computes a rolling sum from a window of rows before and after the current row. See ROLLINGSUM Function.

ROLLINGAVERAGE - computes a rolling average from a window of rows before and after the current row. See ROLLINGAVERAGE Function.

ROWNUMBER - computes the row number for each row, as determined by the ordering column. See ROWNUMBER Function.

The following dataset contains sales data over the final quarter of the year.

Source:

Date

Sales

10/2/16

200

10/9/16

500

10/16/16

350

10/23/16

400

10/30/16

190

11/6/16

550

11/13/16

610

11/20/16

480

11/27/16

660

12/4/16

690

12/11/16

810

12/18/16

950

12/25/16

1020

1/1/17

680

Transform:

First, you want to maintain the row information as a separate column. Since data is ordered already by the Date column, you can use the following:

window value:ROWNUMBER() order:Date

Rename this column to rowId for week of quarter.

Now, you want to extract month and week information from the Date values. Deriving the month value:

derive type:single value:MONTH(Date) as:'Month'

Deriving the quarter value:

derive type:single value:(1 + FLOOR(((month-1)/3))) as:'QTR'

Deriving the week-of-quarter value:

window value:ROWNUMBER() order:Date group:QTR

Rename this column WOQ (week of quarter).

Deriving the week-of-month value:

window value:ROWNUMBER() group:Month order:Date

Rename this column WOM (week of month).

Now, you perform your rolling computations. Compute the running total of sales using the following:

window value: ROLLINGSUM(Sales, -1, 0) order: Date group:QTR

The -1 parameter is used in the above computation to gather the rolling sum of all rows of data from the current one to the first one. Note that the use of the QTR column for grouping, which moves the value for the 01/01/2017 into its own computational bucket. This may or may not be preferred.

Rename this column QTD (quarter to-date). Now, generate a similar column to compute the rolling average of weekly sales for the quarter: